Semantic Prior Analysis for Salient Object Detection

Salient object detection aims to detect the main objects in the given image. In this paper, we propose an approach that integrates semantic priors into the salient object detection process. The method first obtains an explicit saliency map that is refined by the explicit semantic priors learned from...

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Bibliographic Details
Published in:IEEE transactions on image processing 2019-06, Vol.28 (6), p.3130-3141
Main Authors: Nguyen, Tam V., Nguyen, Khanh, Do, Thanh-Toan
Format: Article
Language:English
Subjects:
Computation
Deep learning
deep networks
Image color analysis
Image detection
Object detection
Object recognition
Post-processing
Salience
Saliency detection
Salient object detection
semantic priors
Semantics
Task analysis
Visualization
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Summary:Salient object detection aims to detect the main objects in the given image. In this paper, we propose an approach that integrates semantic priors into the salient object detection process. The method first obtains an explicit saliency map that is refined by the explicit semantic priors learned from data. Then an implicit saliency map is constructed using a trained model that maps the implicit semantic priors embedded into superpixel features with the saliency values. Next, the fusion saliency map is computed by adaptively fusing both the explicit and implicit semantic maps. The final saliency map is eventually computed via the post-processing refinement step. Experimental results have demonstrated the effectiveness of the proposed method; particularly, it achieves competitive performance with the state-of-the-art baselines on three challenging datasets, namely, ECSSD, HKUIS, and iCoSeg.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2019.2894284